Comparative Analysis on the Applicability of Different Typical Year Generating Methods in Solar Energy Resource Assessment

CHANG Rui; SHEN Yanbo; GUO Peng

doi:10.7522/j.issn.1000-0534.2017.00013

2017 , Vol. 36 >Issue 6: 1713 - 1721

DOI: https://doi.org/10.7522/j.issn.1000-0534.2017.00013

Comparative Analysis on the Applicability of Different Typical Year Generating Methods in Solar Energy Resource Assessment

CHANG Rui ,
SHEN Yanbo ,
GUO Peng

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National Climate Center of China Meteorological Administration, Beijing 100081, China;Public Meteorological Service Center of China Meteorological Administration, Beijing 100081, China;Wind and Solar Energy Resources Center of China Meteorological Administration, Beijing 100081, China

Received date: 2016-07-19

Online published: 2017-12-28

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Abstract

Solar energy development and utilization plays an important role in haze governance and fulfilling the reduction commitments in advance. In order to make accurately optimal design and performance evaluation of the solar energy conversion systems, typical meteorological year (TMY) data are needed. A TMY is a data set of daily values of solar radiation and meteorological elements for a 1-year period in this paper. It consists of months selected from individual years and concatenated to form a complete year. In this paper, the Finkelstein-Schafer statistical method, which was initially developed by Sandia national laboratories, is firstly applied by analyzing a 30-year period (1985-2014) daily measured datasets which include global solar radiation, wind speed, relative humidity, air temperature, pressure and dew temperature (an intermediate variable) in order to generate typical meteorological year (TMY) for nine representative meteorological stations in China. Therefore, the TMY data sets obtained here represent conditions judged to be typical over a long period of time (30 years). Meanwhile, the annual global horizontal irradiations (GHI) are also calculated by the normal distribution method and the max probability density method, which are the widely used in the engineering practice. Then, the emphasis is placed on the comparison between Sandia method, the normal distribution method and the max probability density method. It is found that:(1) annual global horizontal irradiations (GHI) calculated from Sandia and normal distribution methods matched well with each other, and it showed a significant deviation in GHI calculated from the max probability density method; (2) despite of the similar annual GHI, significant variations exist in the monthly GHI difference between Sandia and normal distribution methods which was supposed to be connect with the complexity of the local weather conditions; (3) the Sandia method is of the good representative of the typical atmospheric conditions but requires a lot of meteorological observations during the calculation process; (4) the normal distribution method is suitable for quick and effective application since only solar radiation observation is required during the calculation, but it is lack of representative of the local climatic conditions. It is worth noting that because the TMY dataset represents typical case rather than extreme conditions, it is not suited for designing systems and its components to meet the worst condition occurring at a local area. These findings in this paper will be very useful for the performance evaluation of solar energy conversion systems, heating, ventilation and other solar energy dependent systems.

Key words： Solar energy resourc; typical meteorologic; comparative study; daily meteorological

Cite this article

CHANG Rui , SHEN Yanbo , GUO Peng . Comparative Analysis on the Applicability of Different Typical Year Generating Methods in Solar Energy Resource Assessment[J]. Plateau Meteorology, 2017 , 36(6) : 1713 -1721 . DOI: 10.7522/j.issn.1000-0534.2017.00013

References

[1]Crawley D B, 1998. Which weather data should you use for energy simulations of commercial buildings?[J]. ASHRAE Trans, 104:498-515.
[2]Festa R, Ratto C F, 1993. Proposal of a numerical procedure to select reference years[J]. Sol Energy, 50:9-17.
[3]Feuermann D, Gordon J M, Zarmi Y, 1985. A typical meteorological day "TMY" approach for predicting the long-term performance of solar energy systems[J]. J Sol Energy, 35:499-507.
[4]Hall I, Prairie R, Anderson H, et al, 1978. Generation of typical meteorological years for 26 SOLMET stations[R]. SAND78-1601. Albuquerque, NM:Sandia National Laboratories.
[5]Janjai S, Deeyai P, 2009. Comparison of methods for generating typical meteorological year using meteorological data from a tropical environment[J]. Appl Energy, 86:528-537.
[6]Lam J C, Hui S C M, Chan A L S, 1996. A statistical approach to the development of a typical meteorological year for Hong Kong[J]. Architect Sci Rev, 39:201-219.
[7]Marion W, Urban K, 1995. User's manual for TMY2s-typical meteorological years derived from the 1961-1990 national solar radiation data base[M]. Golden, CO:National Renewable Energy Laboratory, 1-55.
[8]Pissimanis D, Karras G, Notaridou V, et al, 1988. The generation of a "typical Meteorological Year" for the city of Athens[J]. J Sol Energy, 40:405-411.
[9]Wilox S, Marion W, 2008. User's manual for TMY3 data sets[R]. NREL Report No. TP-581-41356, 50.
[10]Wilox S, 2007. National solar radiation database 1991-2005 update:User's Manual[R]. NREL Report No. TP-581-41364, 472.
[11]Bai J B, Cao Y, Hao Y Z, et al, 2014. Development of simulation and decision-makings of software of solar PV grid-connected power system[J]. Acta Energiae Solaris Sinica, 34(10):2022-2029. 白建波, 曹阳, 郝玉哲, 等, 2014.光伏并网电站仿真与决策优化软件设计[J].太阳能能学报, 34(10):2022-2029.
[12]Gu X P, Yuan S J, Shi L, et al, 2009. Study on distributed simulation of diffuse solar radiation over complex terrains based on DEM-taking Guizhou Plateau for example[J]. Plateau Meteor, 28(1):143-150. 谷晓平, 袁淑杰, 史岚, 等, 2009.基于DEM的复杂地形下太阳散散射辐射分布式模式——以贵州高原为例[J].高原气象, 28(1):143-150.
[13]Gu J Q, Yang J, Chen H Y, et al, 2008. Development and application of meteorological data on dynamically modeling of energy consumption in dwelling buildings[J]. Acta Energlae Solaris Sinca, 29(1):119-124. 顾骏强, 杨军, 陈海燕, 等, 2008.建筑能耗动态模拟气象资料的开发与应用[J].太阳能学报, 29(1):119-124.
[14]Li H L, Yang L, Liu D L, et al, 2015. Research on the method of generate TMY for building energy consumption simulation[J]. J Xi'an Univ of Arch & Tech (Natural Science Edition), 47(2):267-271. 李红莲, 杨柳, 刘大龙, 等, 2015.建筑能耗模拟用典型气象年产生方法的研究[J].西安建筑科技大学学报(自然科学版), 47(2):267-271.
[15]Li Y Y, Yu R C, Xu Y P, et al, 2003. The formation and diurnal changes of stratiform clouds in southern China[J]. Acta Meteor Sinica, 61(6):733-743. 李昀英, 宇如聪, 徐幼平, 等, 2003.中国南方地区层状云的形成和日变化特征分析[J].气象学报, 61(6):733-743.
[16]Liu K Q, Chen Z H, Xia Z H, 2007. The characteristic analysis and division research of solar energy resource in Hubei province[J], Journal of Huazhong Agricultural University, 26(6):888-893. 刘可群, 陈正洪, 夏智宏, 2007.湖北省太阳能资源时空分布特征及区划研究[J].华中农业大学学报, 26(6):888-893.
[17]Shen Y B, Chang R, Du J, et al, 2015. Analysis of the available solar energy resources in Turpan[J], Plateau Meteor, 36(9):111-115. 申彦波, 常蕊, 杜江, 等, 2015.吐鲁番地区可利用太阳能资源分析[J].高原气象, 34(2):470-477.
[18]Shen Y B, Wang X Y, Wang T, et al, 2016. Frequency characteristics of the global horizontal irradiation in typical cities over China[J]. Wind Energy (8):70-76. 申彦波, 王香云, 王婷, 等, 2016.中国典型地区水平总辐射辐照度频次特征[J].风能(8):70-76.
[19]Shen Y B, 2010. Review of applications of satellite remote sensing data to solar energy resources assessment in China in recent 20 years[J]. Meteor Mon, 36(9):111-115. 申彦波, 2010.近20年卫星遥感资料在我国太阳能资源评估中的应用综述[J].气象, 36(9):111-115.
[20]Wang B Z, Zhang F G, Li L X, 1980. Solar energy resources in China[J]. Acta Energy Solaris Sinica, 1(1):1-9. 王炳忠, 张富国, 李立贤, 1980.我国的太阳能资源及其计算[J].太阳能学报, 1(1):1-9.
[21]Xin Y, Zhao Y Z, Mao W Y, et al, Homogeneity test of the total solar radiation data series and further research on climatological calculation over Xinjiang[J], Plateau Meteor, 30(4):878-889. 辛渝, 赵逸舟, 毛炜峄, 等, 2011.新疆太阳总辐射资料的均一性检验与气候学估算式的再探讨[J].高原气象, 30(4):878-889.
[22]Yang L, Li C H, Liu J P, 2006. Generating method of typical meteorological years and quality analysis of raw meteorological data[J]. Meteor Sci Technol, 34(5):596-599. 杨柳, 李昌华, 刘加平, 2006.典型气象年生成方法及原始气象数据质量分析[J].气象科技, 34(5):596-599.
[23]Zheng Y F, Yin S Y, Wu R J, et al, 2012. Analyses on variation and cause of surface solar radiation in Beijing, Tianjin and Hebei region from 1960 to 2005[J]. Plateau Meteor, 31(2):436-445. 郑有飞, 尹炤寅, 吴荣军, 等, 2012. 1960-2005年京津冀地区地表太阳辐射变化及成因分析[J].高原气象, 31(2):436-445.
[24]Zhou Y, Wu W X, Hu Y, et al, 2010. The temporal-spatial distribution and evaluation of potential solar energy resources in northwest China[J]. Journal of Natural Resources, 25(10):1739-1749. 周扬, 吴文祥, 胡莹, 等, 2010.西北地区太阳能资源空间分布特征及资源潜力评估[J].自然资源学报, 2010, 25(10):1739-1749.
[25]Zhu B, Li C H, Fang F, 2010. Solar energy resource assessment in Gansu province[J]. J Arid Meteor, 28(2):218-221. 朱飙, 李春华, 方锋, 2010.甘肃省太阳能资源评估[J].干旱气象, 28(2):218-221.

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